Topics in Cognitive Science最新文献

Automated moral inference is an emerging topic of critical importance in artificial intelligence. The contemporary approach typically relies on language models to infer moral relevance or moral properties of a concept. This approach demands complex parameterization and costly computation, and it tends to disconnect with existing psychological accounts of moralization. We present a simple cognitive model for moral inference, Moral Association Graph (MAG), inspired by psychological work on moralization. Our model builds on word association network for inferring moral relevance and draws on rich psychological data. We demonstrate that MAG performs competitively to state-of-the-art language models when evaluated against a comprehensive set of data for automated inference of moral norms and moral judgment of concepts, and in-context moral inference. We also show that our model yields interpretable outputs and is applicable to informing short-term moral change.

As they process complex linguistic input, language comprehenders must maintain a mapping between lexical items (e.g., morphemes) and their syntactic position in the sentence. We propose a model of how these morpheme-position bindings are encoded, maintained, and reaccessed in working memory, based on working memory models such as "serial-order-in-a-box" and its SOB-Complex Span version. Like those models, our model of linguistic working memory derives a range of attested memory interference effects from the process of binding items to positions in working memory. We present simulation results capturing similarity-based interference as well as item distortion effects. Our model provides a unified account of these two major classes of interference effects in sentence processing, attributing both types of effects to an associative memory architecture underpinning linguistic computation.

This study delves into how various musical factors influence the experience of auditory illusions, building on Diana Deutsch's scale illusion experiments and subsequent studies. Exploring the interaction between scale mode and timbre, this study assesses their influence on auditory misperceptions, while also considering the impact of an individual's musical training and ability to discern absolute pitch. Participants were divided into nonmusicians, musicians with absolute pitch, and musicians with relative pitch, and were exposed to stimuli modified across three scale modes (tonal, dissonant, atonal) and two timbres (same, different). The findings suggest that scale illusions occur less frequently with different timbres and vary with scale mode. Crucially, the absolute pitch ability appears to have a more significant impact on the perception of illusions than the duration of musical training. This research contributes to understanding the complex interplay between various factors in auditory perception and the mechanisms behind the experience of auditory illusions.

Retrieval practice-the process of actively calling information to mind rather than passively studying materials-has been proven to be a highly effective learning strategy. However, only recently, researchers have started to examine differences between learners in terms of the optimal conditions of retrieval practice in applied educational settings. In this study (N = 118), we focus on learners with dyslexia. We compare their performance to the performance of typical learners in an adaptive retrieval practice task using both typing-based and speech-based response conditions. We find that typical learners outperform learners with dyslexia when they are asked to respond by typing, but that this difference disappears when learners respond by speech. Using a mathematical model to decompose response times, we demonstrate that this typing-specific disadvantage in learners with dyslexia is mainly a consequence of processing delays, rather than poorer memory performance. These findings contribute to a better understanding of the mechanisms underlying declarative learning in dyslexia, and they can be used to tailor educational technology toward the needs of neurodiverse learners.

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground-truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learning from diverse information sources. We first introduce a semi-supervised variant we call the delusional hedge capable of learning from both supervised and unsupervised experiences. In two experiments, we examine the alignment between human judgments and predictions from the standard hedge, the delusional hedge, and a heuristic baseline model. Results indicate that humans effectively incorporate both labeled and unlabeled information in a manner consistent with the delusional hedge algorithm-suggesting that human learners not only gauge the accuracy of information sources but also their consistency with other reliable sources. The findings advance our understanding of human learning from diverse opinions, with implications for the development of algorithms that better capture how people learn to weigh conflicting information sources.

Predictive processing is an influential theoretical framework for understanding human and animal cognition. In the context of predictive processing, learning is often reduced to optimizing the parameters of a generative model with a predefined structure. This is known as Bayesian parameter learning. However, to provide a comprehensive account of learning, one must also explain how the brain learns the structure of its generative model. This second kind of learning is known as structure learning. Structure learning would involve true structural changes in generative models. The purpose of the current paper is to describe the processes involved upstream of these structural changes. To do this, we first highlight the remarkable compatibility between predictive processing and the processing fluency theory. More precisely, we argue that predictive processing is able to account for all the main theoretical constructs associated with the notion of processing fluency (i.e., the fluency heuristic, naïve theory, the discrepancy-attribution hypothesis, absolute fluency, expected fluency, and relative fluency). We then use this predictive processing account of processing fluency to show how the brain could infer whether it needs a structural change for learning the causal regularities at play in the environment. Finally, we speculate on how this inference might indirectly trigger structural changes when necessary.

The 1998 article by van Gelder proposed a Dynamical Hypothesis (DH) in cognitive science consisting of Nature (cognitive agents are dynamical systems) and Knowledge (cognitive agents should be understood dynamically) hypotheses in contrast to the Computational Hypothesis (CH) that cognitive agents are computers. My commentary focuses on the contributions of Paxton and Necaise et al. in interpersonal motor coordination and radicalization across social media. I do not think that either contribution supports the Nature hypothesis but does conform with the Knowledge hypothesis. I conclude by describing cognitive agents as living systems (or nonliving systems that mimic aspects of living systems) that can be alternately viewed to support the DH or CH or both at the same time.

The interaction-dominant approach to perception and action, originally formulated in the mid-1990s, has matured and gained remarkable momentum as an entailment of the dynamical hypotheses proposed at that time. This framework seeks to explain the fluid and intricate interplay of causality spanning the entire organism by integrating high-dimensional details with low-dimensional constraints across various scales of behavior. Both Chemero (2024) and Wallot et al. (2024) have skillfully explored the theoretical implications and methodological challenges this perspective introduces. We echo Chemero's (2024) and Wallot et al.'s (2024) focus on multifractality, while also underscoring new efforts to model the synergetic relationships and cascading dynamics inherent in this interaction-dominant approach.

About 30 years ago, the Dynamical Hypothesis instigated a variety of insights and transformations in cognitive science. One of them was the simple observation that, quite unlike trial-based tasks in a laboratory, natural ecologically valid behaviors almost never have context-free starting points. Instead, they produce lengthy time series data that can be recorded with dense-sampling measures, such as heartrate, eye movements, EEG, etc. That emphasis on studying the temporal dynamics of extended behaviors may have been the trigger that led to a rethinking of what a "representation" is, and then of what a "cognitive agent" is. This most recent and perhaps most revolutionary transformation is the idea that a cognitive agent need not be a singular physiological organism. Perhaps a group of organisms, such as several people working on a joint task, can temporarily function as one cognitive agent - at least while they're working adaptively and successfully.